ASMase Activation in Ultrasound-Stimulated Radiation Enhancement Using MRI-Guided Focused Ultrasound
Bibliographic record
Abstract
Focused ultrasound-stimulated microbubble (MB + FUS) therapy is a promising radiation enhancement strategy, utilizing vascular disruption to enhance radiation efficacy. However, its mechanistic effects in large tumour volumes and clinical translatability remain insufficiently characterized. This study evaluates the synergistic impact of MB + FUS combined with radiation therapy (XRT) in a large-scale, immunosuppressed rabbit xenograft model using a clinically adaptable, MRI-guided 6144-element focused ultrasound (MRgFUS) system. Tumours were treated with MB + FUS, XRT, or both, with real-time image-guided MB activation and evaluation of treatment effects on vascular integrity, proliferation, and cellular stress responses. Assessments included Power Doppler ultrasound, histology, and immunohistochemistry targeting TUNEL, ASMase, Ki-67, Factor VIII, HIF-1α, and VEGF. Combination therapy induced significant vascular collapse, reduced perfusion, and decreased Factor VIII expression, alongside increased nuclear condensation, TUNEL positivity, and ASMase expression, consistent with ASMase-mediated endothelial apoptosis and vascular disruption. Upregulation of HIF-1α and VEGF indicated hypoxia-driven angiogenic signalling, while Ki-67 suppression reflected sustained tumour growth inhibition. Although immune responses were limited by host immunosuppression, the larger tumour burden provided clinically relevant constraints. The MRgFUS platform enabled precise and reproducible MB activation, reinforcing MB + FUS as a potent radio-enhancement modality. These findings support the continued development of MB + FUS toward clinical translation and highlight its potential as a complementary strategy to radiation therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".